FastMRI

doi:doi:10.57702/0pzii9bm

FastMRI

Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based methods have been shown to produce superior performance on MR image reconstruction. However, these methods require large amounts of data which is difficult to collect and share due to the high cost of acquisition and medical data privacy regulations.

BibTex:

@dataset{F_K_Zbontar_and_K_Hammernik_and_M_Lustig_and_M_Jacob_2024,
    abstract = {Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based methods have been shown to produce superior performance on MR image reconstruction. However, these methods require large amounts of data which is difficult to collect and share due to the high cost of acquisition and medical data privacy regulations.},
    author = {F. K. Zbontar and K. Hammernik and M. Lustig and M. Jacob},
    doi = {10.57702/0pzii9bm},
    institution = {No Organization},
    keyword = {'Accelerated MRI', 'Accelerated Reconstruction', 'Deep Learning', 'FastMRI', 'Image Reconstruction', 'Knee Images', 'MRI', 'Machine Learning', 'Magnetic Resonance Imaging', 'Under-sampled Data', 'reconstruction', 'undersampled MRI'},
    month = {dec},
    publisher = {TIB},
    title = {FastMRI},
    url = {https://service.tib.eu/ldmservice/dataset/fastmri},
    year = {2024}
}